Search engines include a plurality of features in order to provide a forecast for user's query. Such forecast may include query auto-complete and search suggestions. Nowadays, such forecast methods are based on historic keywords references. Such historic references may not be accurate because one keyword could be referred to a plurality of topics in a single text.
In addition, user search queries may include one or more entities identified by name or attributes that may be associated with the entity. Entities may also include organizations, people, locations, events, date and/or time. In a typical search, if a user is searching for information related to two particular organizations, a search engine may return assorted results that may be about a mixture of different entities with the same name or similar names. The latter approach may lead the user to find a very large amount of documents that may not be relevant to what the user is actually interested.
Thus, a need exists for a method for obtaining quicker and more accurate search suggestions.